# coding=utf-8
# Copyright 2018 The Google AI Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python2, python3

# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
# limitations under the License.

"""The main ALBERT model and related functions.

For a description of the algorithm, see https://arxiv.org/abs/1909.11942.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import copy
import json
import math
import re

import numpy as np
import six
import tensorflow.compat.v1 as tf
from six.moves import range
from tensorflow.contrib import layers as contrib_layers


class BertConfig(object):
    """Configuration for `AlbertModel`.

    The default settings match the configuration of model `albert_xxlarge`.
    """

    def __init__(self,
                 vocab_size,
                 embedding_size=128,
                 hidden_size=4096,
                 num_hidden_layers=12,
                 num_hidden_groups=1,
                 num_attention_heads=64,
                 intermediate_size=16384,
                 inner_group_num=1,
                 down_scale_factor=1,
                 hidden_act="gelu",
                 hidden_dropout_prob=0,
                 attention_probs_dropout_prob=0,
                 max_position_embeddings=512,
                 type_vocab_size=2,
                 initializer_range=0.02):
        """Constructs AlbertConfig.

        Args:
            vocab_size: Vocabulary size of `inputs_ids` in `AlbertModel`.
            embedding_size: size of voc embeddings.
            hidden_size: Size of the encoder layers and the pooler layer.
            num_hidden_layers: Number of hidden layers in the Transformer encoder.
            num_hidden_groups: Number of group for the hidden layers, parameters in
                the same group are shared.
            num_attention_heads: Number of attention heads for each attention layer in
                the Transformer encoder.
            intermediate_size: The size of the "intermediate" (i.e., feed-forward)
                layer in the Transformer encoder.
            inner_group_num: int, number of inner repetition of attention and ffn.
            down_scale_factor: float, the scale to apply
            hidden_act: The non-linear activation function (function or string) in the
                encoder and pooler.
            hidden_dropout_prob: The dropout probability for all fully connected
                layers in the embeddings, encoder, and pooler.
            attention_probs_dropout_prob: The dropout ratio for the attention
                probabilities.
            max_position_embeddings: The maximum sequence length that this model might
                ever be used with. Typically set this to something large just in case
                (e.g., 512 or 1024 or 2048).
            type_vocab_size: The vocabulary size of the `token_type_ids` passed into
                `AlbertModel`.
            initializer_range: The stdev of the truncated_normal_initializer for
                initializing all weight matrices.
        """
        self.vocab_size = vocab_size
        self.embedding_size = embedding_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_hidden_groups = num_hidden_groups
        self.num_attention_heads = num_attention_heads
        self.inner_group_num = inner_group_num
        self.down_scale_factor = down_scale_factor
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range

    @classmethod
    def from_dict(cls, json_object):
        """Constructs a `AlbertConfig` from a Python dictionary of parameters."""
        config = BertConfig(vocab_size=None)
        for (key, value) in six.iteritems(json_object):
            config.__dict__[key] = value
        return config

    @classmethod
    def from_json_file(cls, json_file):
        """Constructs a `AlbertConfig` from a json file of parameters."""
        with tf.gfile.GFile(json_file, "r") as reader:
            text = reader.read()
        return cls.from_dict(json.loads(text))

    def to_dict(self):
        """Serializes this instance to a Python dictionary."""
        output = copy.deepcopy(self.__dict__)
        return output

    def to_json_string(self):
        """Serializes this instance to a JSON string."""
        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"


class BertModel(object):
    """BERT model ("Bidirectional Encoder Representations from Transformers").

    Example usage:

    ```python
    # Already been converted from strings into ids
    input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
    input_mask = tf.constant([[1, 1, 1], [1, 1, 0]])
    token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])

    config = modeling.AlbertConfig(vocab_size=32000, hidden_size=512,
    num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)

    model = modeling.AlbertModel(config=config, is_training=True,
    input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids)

    label_embeddings = tf.get_variable(...)
    pooled_output = model.get_pooled_output()
    logits = tf.matmul(pooled_output, label_embeddings)
    ...
    ```
    """

    def __init__(self,
                 config,
                 input_ids,
                 input_mask=None,
                 token_type_ids=None,
                 use_one_hot_embeddings=False,
                 use_einsum=True,
                 scope=None):
        """Constructor for AlbertModel.

        Args:
            config: `AlbertConfig` instance.
            input_ids: int32 Tensor of shape [batch_size, seq_length].
            input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
            token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
            use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
                embeddings or tf.embedding_lookup() for the word embeddings.
            use_einsum: (optional) bool. Whether to use einsum or reshape+matmul for
                dense layers
            scope: (optional) variable scope. Defaults to "bert".

        Raises:
            ValueError: The config is invalid or one of the input tensor shapes
                is invalid.
        """
        config = copy.deepcopy(config)
        config.hidden_dropout_prob = 0.0
        config.attention_probs_dropout_prob = 0.0

        input_shape = get_shape_list(input_ids, expected_rank=2)
        batch_size = input_shape[0]
        seq_length = input_shape[1]

        if input_mask is None:
            input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32)

        if token_type_ids is None:
            token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32)

        with tf.variable_scope(scope, default_name="bert"):
            with tf.variable_scope("embeddings"):
                # Perform embedding lookup on the word ids.
                (self.word_embedding_output,
                 self.output_embedding_table) = embedding_lookup(
                    input_ids=input_ids,
                    vocab_size=config.vocab_size,
                    embedding_size=config.embedding_size,
                    initializer_range=config.initializer_range,
                    word_embedding_name="word_embeddings",
                    use_one_hot_embeddings=use_one_hot_embeddings)

                # Add positional embeddings and token type embeddings, then layer
                # normalize and perform dropout.
                self.embedding_output = embedding_postprocessor(
                    input_tensor=self.word_embedding_output,
                    use_token_type=True,
                    token_type_ids=token_type_ids,
                    token_type_vocab_size=config.type_vocab_size,
                    token_type_embedding_name="token_type_embeddings",
                    use_position_embeddings=True,
                    position_embedding_name="position_embeddings",
                    initializer_range=config.initializer_range,
                    max_position_embeddings=config.max_position_embeddings,
                    dropout_prob=config.hidden_dropout_prob,
                    use_one_hot_embeddings=use_one_hot_embeddings)

            with tf.variable_scope("encoder"):
                # Run the stacked transformer.
                # `sequence_output` shape = [batch_size, seq_length, hidden_size].
                self.all_encoder_layers = transformer_model(
                    input_tensor=self.embedding_output,
                    attention_mask=input_mask,
                    hidden_size=config.hidden_size,
                    num_hidden_layers=config.num_hidden_layers,
                    num_hidden_groups=config.num_hidden_groups,
                    num_attention_heads=config.num_attention_heads,
                    intermediate_size=config.intermediate_size,
                    inner_group_num=config.inner_group_num,
                    intermediate_act_fn=get_activation(config.hidden_act),
                    hidden_dropout_prob=config.hidden_dropout_prob,
                    attention_probs_dropout_prob=config.attention_probs_dropout_prob,
                    initializer_range=config.initializer_range,
                    do_return_all_layers=True,
                    use_einsum=use_einsum)

            self.sequence_output = self.all_encoder_layers[-1]
            # The "pooler" converts the encoded sequence tensor of shape
            # [batch_size, seq_length, hidden_size] to a tensor of shape
            # [batch_size, hidden_size]. This is necessary for segment-level
            # (or segment-pair-level) classification tasks where we need a fixed
            # dimensional representation of the segment.
            with tf.variable_scope("pooler"):
                # We "pool" the model by simply taking the hidden state corresponding
                # to the first token. We assume that this has been pre-trained
                first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1)
                self.pooled_output = tf.layers.dense(
                    first_token_tensor,
                    config.hidden_size,
                    activation=tf.tanh,
                    kernel_initializer=create_initializer(config.initializer_range))

    def get_pooled_output(self):
        return self.pooled_output

    def get_sequence_output(self):
        """Gets final hidden layer of encoder.

        Returns:
            float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
            to the final hidden of the transformer encoder.
        """
        return self.sequence_output

    def get_all_encoder_layers(self):
        return self.all_encoder_layers

    def get_word_embedding_output(self):
        """Get output of the word(piece) embedding lookup.

        This is BEFORE positional embeddings and token type embeddings have been
        added.

        Returns:
            float Tensor of shape [batch_size, seq_length, embedding_size]
            corresponding to the output of the word(piece) embedding layer.
        """
        return self.word_embedding_output

    def get_embedding_output(self):
        """Gets output of the embedding lookup (i.e., input to the transformer).

        Returns:
            float Tensor of shape [batch_size, seq_length, embedding_size]
            corresponding to the output of the embedding layer, after summing the word
            embeddings with the positional embeddings and the token type embeddings,
            then performing layer normalization. This is the input to the transformer.
        """
        return self.embedding_output

    def get_embedding_table(self):
        return self.output_embedding_table


def gelu(x):
    """Gaussian Error Linear Unit.

    This is a smoother version of the RELU.
    Original paper: https://arxiv.org/abs/1606.08415
    Args:
        x: float Tensor to perform activation.

    Returns:
        `x` with the GELU activation applied.
    """
    cdf = 0.5 * (1.0 + tf.tanh(
        (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
    return x * cdf


def get_activation(activation_string):
    """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`.

    Args:
        activation_string: String name of the activation function.

    Returns:
        A Python function corresponding to the activation function. If
        `activation_string` is None, empty, or "linear", this will return None.
        If `activation_string` is not a string, it will return `activation_string`.

    Raises:
        ValueError: The `activation_string` does not correspond to a known
            activation.
    """

    # We assume that anything that"s not a string is already an activation
    # function, so we just return it.
    if not isinstance(activation_string, six.string_types):
        return activation_string

    if not activation_string:
        return None

    act = activation_string.lower()
    if act == "linear":
        return None
    elif act == "relu":
        return tf.nn.relu
    elif act == "gelu":
        return gelu
    elif act == "tanh":
        return tf.tanh
    else:
        raise ValueError("Unsupported activation: %s" % act)


def get_assignment_map_from_checkpoint(tvars, init_checkpoint, num_of_group=0):
    """Compute the union of the current variables and checkpoint variables."""

    initialized_variable_names = {}

    name_to_variable = collections.OrderedDict()
    for var in tvars:
        name = var.name
        m = re.match("^(.*):\\d+$", name)
        if m is not None:
            name = m.group(1)
        name_to_variable[name] = var
    init_vars = tf.train.list_variables(init_checkpoint)
    init_vars_name = [name for (name, _) in init_vars]

    if num_of_group > 0:
        assignment_map = []
        for gid in range(num_of_group):
            assignment_map.append(collections.OrderedDict())
    else:
        assignment_map = collections.OrderedDict()

    for name in name_to_variable:
        if name in init_vars_name:
            tvar_name = name
        elif (re.sub(r"/group_\d+/", "/group_0/",
                     six.ensure_str(name)) in init_vars_name and
              num_of_group > 1):
            tvar_name = re.sub(r"/group_\d+/", "/group_0/", six.ensure_str(name))
        elif (re.sub(r"/ffn_\d+/", "/ffn_1/", six.ensure_str(name))
              in init_vars_name and num_of_group > 1):
            tvar_name = re.sub(r"/ffn_\d+/", "/ffn_1/", six.ensure_str(name))
        elif (re.sub(r"/attention_\d+/", "/attention_1/", six.ensure_str(name))
              in init_vars_name and num_of_group > 1):
            tvar_name = re.sub(r"/attention_\d+/", "/attention_1/",
                               six.ensure_str(name))
        else:
            continue
        if num_of_group > 0:
            group_matched = False
            for gid in range(1, num_of_group):
                if (("/group_" + str(gid) + "/" in name) or
                        ("/ffn_" + str(gid) + "/" in name) or
                        ("/attention_" + str(gid) + "/" in name)):
                    group_matched = True
                    assignment_map[gid][tvar_name] = name
            if not group_matched:
                assignment_map[0][tvar_name] = name
        else:
            assignment_map[tvar_name] = name
        initialized_variable_names[name] = 1
        initialized_variable_names[six.ensure_str(name) + ":0"] = 1

    return assignment_map, initialized_variable_names


def dropout(input_tensor, dropout_prob):
    """Perform dropout.

    Args:
        input_tensor: float Tensor.
        dropout_prob: Python float. The probability of dropping out a value (NOT of
            *keeping* a dimension as in `tf.nn.dropout`).

    Returns:
        A version of `input_tensor` with dropout applied.
    """
    if dropout_prob is None or dropout_prob == 0.0:
        return input_tensor

    output = tf.nn.dropout(input_tensor, rate=dropout_prob)
    return output


def layer_norm(input_tensor, name=None):
    """Run layer normalization on the last dimension of the tensor."""
    return contrib_layers.layer_norm(
        inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)


def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
    """Runs layer normalization followed by dropout."""
    output_tensor = layer_norm(input_tensor, name)
    output_tensor = dropout(output_tensor, dropout_prob)
    return output_tensor


def create_initializer(initializer_range=0.02):
    """Creates a `truncated_normal_initializer` with the given range."""
    return tf.truncated_normal_initializer(stddev=initializer_range)


def get_timing_signal_1d_given_position(channels, position, min_timescale=1.0, max_timescale=1.0e4):
    """Get sinusoids of diff frequencies, with timing position given.

    Adapted from add_timing_signal_1d_given_position in
    //third_party/py/tensor2tensor/layers/common_attention.py

    Args:
        channels: scalar, size of timing embeddings to create. The number of
            different timescales is equal to channels / 2.
        position: a Tensor with shape [batch, seq_len]
        min_timescale: a float
        max_timescale: a float

    Returns:
        a Tensor of timing signals [batch, seq_len, channels]
    """
    num_timescales = channels // 2
    log_timescale_increment = (
            math.log(float(max_timescale) / float(min_timescale)) /
            (tf.to_float(num_timescales) - 1))
    inv_timescales = min_timescale * tf.exp(
        tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
    scaled_time = (
            tf.expand_dims(tf.to_float(position), 2) * tf.expand_dims(tf.expand_dims(inv_timescales, 0), 0))
    signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=2)
    signal = tf.pad(signal, [[0, 0], [0, 0], [0, tf.mod(channels, 2)]])
    return signal


def embedding_lookup(input_ids,
                     vocab_size,
                     embedding_size=128,
                     initializer_range=0.02,
                     word_embedding_name="word_embeddings",
                     use_one_hot_embeddings=False):
    """Looks up words embeddings for id tensor.

    Args:
        input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
            ids.
        vocab_size: int. Size of the embedding vocabulary.
        embedding_size: int. Width of the word embeddings.
        initializer_range: float. Embedding initialization range.
        word_embedding_name: string. Name of the embedding table.
        use_one_hot_embeddings: bool. If True, use one-hot method for word
            embeddings. If False, use `tf.nn.embedding_lookup()`.

    Returns:
        float Tensor of shape [batch_size, seq_length, embedding_size].
    """
    # This function assumes that the input is of shape [batch_size, seq_length,
    # num_inputs].
    #
    # If the input is a 2D tensor of shape [batch_size, seq_length], we
    # reshape to [batch_size, seq_length, 1].
    if input_ids.shape.ndims == 2:
        input_ids = tf.expand_dims(input_ids, axis=[-1])

    embedding_table = tf.get_variable(
        name=word_embedding_name,
        shape=[vocab_size, embedding_size],
        initializer=create_initializer(initializer_range))

    if use_one_hot_embeddings:
        flat_input_ids = tf.reshape(input_ids, [-1])
        one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
        output = tf.matmul(one_hot_input_ids, embedding_table)
    else:
        output = tf.nn.embedding_lookup(embedding_table, input_ids)

    input_shape = get_shape_list(input_ids)

    output = tf.reshape(output,
                        input_shape[0:-1] + [input_shape[-1] * embedding_size])
    return output, embedding_table


def embedding_postprocessor(input_tensor,
                            use_token_type=False,
                            token_type_ids=None,
                            token_type_vocab_size=16,
                            token_type_embedding_name="token_type_embeddings",
                            use_position_embeddings=True,
                            position_embedding_name="position_embeddings",
                            initializer_range=0.02,
                            max_position_embeddings=512,
                            dropout_prob=0.1,
                            use_one_hot_embeddings=True):
    """Performs various post-processing on a word embedding tensor.

    Args:
        input_tensor: float Tensor of shape [batch_size, seq_length,
            embedding_size].
        use_token_type: bool. Whether to add embeddings for `token_type_ids`.
        token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
            Must be specified if `use_token_type` is True.
        token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
        token_type_embedding_name: string. The name of the embedding table variable
            for token type ids.
        use_position_embeddings: bool. Whether to add position embeddings for the
            position of each token in the sequence.
        position_embedding_name: string. The name of the embedding table variable
            for positional embeddings.
        initializer_range: float. Range of the weight initialization.
        max_position_embeddings: int. Maximum sequence length that might ever be
            used with this model. This can be longer than the sequence length of
            input_tensor, but cannot be shorter.
        dropout_prob: float. Dropout probability applied to the final output tensor.
        use_one_hot_embeddings: bool. If True, use one-hot method for word
            embeddings. If False, use `tf.nn.embedding_lookup()`.

    Returns:
        float tensor with same shape as `input_tensor`.

    Raises:
        ValueError: One of the tensor shapes or input values is invalid.
    """
    input_shape = get_shape_list(input_tensor, expected_rank=3)
    batch_size = input_shape[0]
    seq_length = input_shape[1]
    width = input_shape[2]

    output = input_tensor

    if use_token_type:
        if token_type_ids is None:
            raise ValueError("`token_type_ids` must be specified if"
                             "`use_token_type` is True.")
        token_type_table = tf.get_variable(
            name=token_type_embedding_name,
            shape=[token_type_vocab_size, width],
            initializer=create_initializer(initializer_range))
        # This vocab will be small so we always do one-hot here, since it is always
        # faster for a small vocabulary, unless converting to tflite model.
        if use_one_hot_embeddings:
            flat_token_type_ids = tf.reshape(token_type_ids, [-1])
            one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
            token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
            token_type_embeddings = tf.reshape(token_type_embeddings,
                                               [batch_size, seq_length, width])
        else:
            token_type_embeddings = tf.nn.embedding_lookup(token_type_table,
                                                           token_type_ids)
        output += token_type_embeddings

    if use_position_embeddings:
        assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
        with tf.control_dependencies([assert_op]):
            full_position_embeddings = tf.get_variable(
                name=position_embedding_name,
                shape=[max_position_embeddings, width],
                initializer=create_initializer(initializer_range))
            # Since the position embedding table is a learned variable, we create it
            # using a (long) sequence length `max_position_embeddings`. The actual
            # sequence length might be shorter than this, for faster training of
            # tasks that do not have long sequences.
            #
            # So `full_position_embeddings` is effectively an embedding table
            # for position [0, 1, 2, ..., max_position_embeddings-1], and the current
            # sequence has positions [0, 1, 2, ... seq_length-1], so we can just
            # perform a slice.
            position_embeddings = tf.slice(full_position_embeddings, [0, 0],
                                           [seq_length, -1])
            num_dims = len(output.shape.as_list())

            # Only the last two dimensions are relevant (`seq_length` and `width`), so
            # we broadcast among the first dimensions, which is typically just
            # the batch size.
            position_broadcast_shape = []
            for _ in range(num_dims - 2):
                position_broadcast_shape.append(1)
            position_broadcast_shape.extend([seq_length, width])
            position_embeddings = tf.reshape(position_embeddings,
                                             position_broadcast_shape)
            output += position_embeddings

    output = layer_norm_and_dropout(output, dropout_prob)
    return output


def einsum_via_matmul(input_tensor, w, num_inner_dims):
    """Implements einsum via matmul and reshape ops.

    Args:
        input_tensor: float Tensor of shape [<batch_dims>, <inner_dims>].
        w: float Tensor of shape [<inner_dims>, <outer_dims>].
        num_inner_dims: int. number of dimensions to use for inner products.

    Returns:
        float Tensor of shape [<batch_dims>, <outer_dims>].
    """
    input_shape = get_shape_list(input_tensor)
    w_shape = get_shape_list(w)
    batch_dims = input_shape[: -num_inner_dims]
    inner_dims = input_shape[-num_inner_dims:]
    outer_dims = w_shape[num_inner_dims:]
    inner_dim = np.prod(inner_dims)
    outer_dim = np.prod(outer_dims)
    if num_inner_dims > 1:
        input_tensor = tf.reshape(input_tensor, batch_dims + [inner_dim])
    if len(w_shape) > 2:
        w = tf.reshape(w, [inner_dim, outer_dim])
    ret = tf.matmul(input_tensor, w)
    if len(outer_dims) > 1:
        ret = tf.reshape(ret, batch_dims + outer_dims)
    return ret


def dense_layer_3d(input_tensor,
                   num_attention_heads,
                   head_size,
                   initializer,
                   activation,
                   use_einsum,
                   name=None):
    """A dense layer with 3D kernel.

    Args:
        input_tensor: float Tensor of shape [batch, seq_length, hidden_size].
        num_attention_heads: Number of attention heads.
        head_size: The size per attention head.
        initializer: Kernel initializer.
        activation: Actication function.
        use_einsum: bool. Whether to use einsum or reshape+matmul for dense layers.
        name: The name scope of this layer.

    Returns:
        float logits Tensor.
    """

    input_shape = get_shape_list(input_tensor)
    hidden_size = input_shape[2]

    with tf.variable_scope(name):
        w = tf.get_variable(
            name="kernel",
            shape=[hidden_size, num_attention_heads * head_size],
            initializer=initializer)
        w = tf.reshape(w, [hidden_size, num_attention_heads, head_size])
        b = tf.get_variable(
            name="bias",
            shape=[num_attention_heads * head_size],
            initializer=tf.zeros_initializer)
        b = tf.reshape(b, [num_attention_heads, head_size])
        if use_einsum:
            ret = tf.einsum("BFH,HND->BFND", input_tensor, w)
        else:
            ret = einsum_via_matmul(input_tensor, w, 1)
        ret += b
    if activation is not None:
        return activation(ret)
    else:
        return ret


def dense_layer_3d_proj(input_tensor,
                        hidden_size,
                        head_size,
                        initializer,
                        activation,
                        use_einsum,
                        name=None):
    """A dense layer with 3D kernel for projection.

    Args:
        input_tensor: float Tensor of shape [batch,from_seq_length,
            num_attention_heads, size_per_head].
        hidden_size: The size of hidden layer.
        head_size: The size of head.
        initializer: Kernel initializer.
        activation: Actication function.
        use_einsum: bool. Whether to use einsum or reshape+matmul for dense layers.
        name: The name scope of this layer.

    Returns:
        float logits Tensor.
    """
    input_shape = get_shape_list(input_tensor)
    num_attention_heads = input_shape[2]
    with tf.variable_scope(name):
        w = tf.get_variable(
            name="kernel",
            shape=[num_attention_heads * head_size, hidden_size],
            initializer=initializer)
        w = tf.reshape(w, [num_attention_heads, head_size, hidden_size])
        b = tf.get_variable(
            name="bias", shape=[hidden_size], initializer=tf.zeros_initializer)
        if use_einsum:
            ret = tf.einsum("BFND,NDH->BFH", input_tensor, w)
        else:
            ret = einsum_via_matmul(input_tensor, w, 2)
        ret += b
    if activation is not None:
        return activation(ret)
    else:
        return ret


def dense_layer_2d(input_tensor,
                   output_size,
                   initializer,
                   activation,
                   use_einsum,
                   num_attention_heads=1,
                   name=None):
    """A dense layer with 2D kernel.

    Args:
        input_tensor: Float tensor with rank 3.
        output_size: The size of output dimension.
        initializer: Kernel initializer.
        activation: Activation function.
        use_einsum: bool. Whether to use einsum or reshape+matmul for dense layers.
        num_attention_heads: number of attention head in attention layer.
        name: The name scope of this layer.

    Returns:
        float logits Tensor.
    """
    del num_attention_heads  # unused
    input_shape = get_shape_list(input_tensor)
    hidden_size = input_shape[2]
    with tf.variable_scope(name):
        w = tf.get_variable(
            name="kernel",
            shape=[hidden_size, output_size],
            initializer=initializer)
        b = tf.get_variable(
            name="bias", shape=[output_size], initializer=tf.zeros_initializer)
        if use_einsum:
            ret = tf.einsum("BFH,HO->BFO", input_tensor, w)
        else:
            ret = tf.matmul(input_tensor, w)
        ret += b
    if activation is not None:
        return activation(ret)
    else:
        return ret


def dot_product_attention(q, k, v, bias, dropout_rate=0.0):
    """Dot-product attention.

    Args:
        q: Tensor with shape [..., length_q, depth_k].
        k: Tensor with shape [..., length_kv, depth_k]. Leading dimensions must
            match with q.
        v: Tensor with shape [..., length_kv, depth_v] Leading dimensions must
            match with q.
        bias: bias Tensor (see attention_bias())
        dropout_rate: a float.

    Returns:
        Tensor with shape [..., length_q, depth_v].
    """
    logits = tf.matmul(q, k, transpose_b=True)  # [..., length_q, length_kv]
    logits = tf.multiply(logits, 1.0 / math.sqrt(float(get_shape_list(q)[-1])))
    if bias is not None:
        # `attention_mask` = [B, T]
        from_shape = get_shape_list(q)
        if len(from_shape) == 4:
            broadcast_ones = tf.ones([from_shape[0], 1, from_shape[2], 1], tf.float32)
        elif len(from_shape) == 5:
            # from_shape = [B, N, Block_num, block_size, depth]#
            broadcast_ones = tf.ones([from_shape[0], 1, from_shape[2], from_shape[3],
                                      1], tf.float32)

        bias = tf.matmul(broadcast_ones, tf.cast(bias, tf.float32), transpose_b=True)

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
        # positions we want to attend and -10000.0 for masked positions.
        adder = (1.0 - bias) * -10000.0

        # Since we are adding it to the raw scores before the softmax, this is
        # effectively the same as removing these entirely.
        logits += adder
    else:
        adder = 0.0

    attention_probs = tf.nn.softmax(logits, name="attention_probs")
    attention_probs = dropout(attention_probs, dropout_rate)
    return tf.matmul(attention_probs, v)


def attention_layer(from_tensor,
                    to_tensor,
                    attention_mask=None,
                    num_attention_heads=1,
                    query_act=None,
                    key_act=None,
                    value_act=None,
                    attention_probs_dropout_prob=0.0,
                    initializer_range=0.02,
                    batch_size=None,
                    from_seq_length=None,
                    to_seq_length=None,
                    use_einsum=True):
    """Performs multi-headed attention from `from_tensor` to `to_tensor`.

    Args:
        from_tensor: float Tensor of shape [batch_size, from_seq_length,
            from_width].
        to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
        attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
            The values should be 1 or 0. The attention scores will effectively
            be set to -infinity for any positions in the mask that are 0, and
            will be unchanged for positions that are 1.
        num_attention_heads: int. Number of attention heads.
        query_act: (optional) Activation function for the query transform.
        key_act: (optional) Activation function for the key transform.
        value_act: (optional) Activation function for the value transform.
        attention_probs_dropout_prob: (optional) float. Dropout probability of the
            attention probabilities.
        initializer_range: float. Range of the weight initializer.
        batch_size: (Optional) int. If the input is 2D, this might be the batch size
            of the 3D version of the `from_tensor` and `to_tensor`.
        from_seq_length: (Optional) If the input is 2D, this might be the seq length
            of the 3D version of the `from_tensor`.
        to_seq_length: (Optional) If the input is 2D, this might be the seq length
            of the 3D version of the `to_tensor`.
        use_einsum: bool. Whether to use einsum or reshape+matmul for dense layers

    Returns:
        float Tensor of shape [batch_size, from_seq_length, num_attention_heads,
        size_per_head].

    Raises:
        ValueError: Any of the arguments or tensor shapes are invalid.
    """
    from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
    to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])
    size_per_head = int(from_shape[2] / num_attention_heads)

    if len(from_shape) != len(to_shape):
        raise ValueError(
            "The rank of `from_tensor` must match the rank of `to_tensor`.")

    if len(from_shape) == 3:
        batch_size = from_shape[0]
        from_seq_length = from_shape[1]
        to_seq_length = to_shape[1]
    elif len(from_shape) == 2:
        if batch_size is None or from_seq_length is None or to_seq_length is None:
            raise ValueError(
                "When passing in rank 2 tensors to attention_layer, the values "
                "for `batch_size`, `from_seq_length`, and `to_seq_length` "
                "must all be specified.")

    # Scalar dimensions referenced here:
    #   B = batch size (number of sequences)
    #   F = `from_tensor` sequence length
    #   T = `to_tensor` sequence length
    #   N = `num_attention_heads`
    #   H = `size_per_head`

    # `query_layer` = [B, F, N, H]
    q = dense_layer_3d(from_tensor, num_attention_heads, size_per_head,
                       create_initializer(initializer_range), query_act,
                       use_einsum, "query")

    # `key_layer` = [B, T, N, H]
    k = dense_layer_3d(to_tensor, num_attention_heads, size_per_head,
                       create_initializer(initializer_range), key_act,
                       use_einsum, "key")
    # `value_layer` = [B, T, N, H]
    v = dense_layer_3d(to_tensor, num_attention_heads, size_per_head,
                       create_initializer(initializer_range), value_act,
                       use_einsum, "value")
    q = tf.transpose(q, [0, 2, 1, 3])
    k = tf.transpose(k, [0, 2, 1, 3])
    v = tf.transpose(v, [0, 2, 1, 3])
    if attention_mask is not None:
        attention_mask = tf.reshape(
            attention_mask, [batch_size, 1, to_seq_length, 1])
        # 'new_embeddings = [B, N, F, H]'
    new_embeddings = dot_product_attention(q, k, v, attention_mask,
                                           attention_probs_dropout_prob)

    return tf.transpose(new_embeddings, [0, 2, 1, 3])


def attention_ffn_block(layer_input,
                        hidden_size=768,
                        attention_mask=None,
                        num_attention_heads=1,
                        attention_head_size=64,
                        attention_probs_dropout_prob=0.0,
                        intermediate_size=3072,
                        intermediate_act_fn=None,
                        initializer_range=0.02,
                        hidden_dropout_prob=0.0,
                        use_einsum=True):
    """A network with attention-ffn as sub-block.

    Args:
        layer_input: float Tensor of shape [batch_size, from_seq_length,
            from_width].
        hidden_size: (optional) int, size of hidden layer.
        attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
            The values should be 1 or 0. The attention scores will effectively be set
            to -infinity for any positions in the mask that are 0, and will be
            unchanged for positions that are 1.
        num_attention_heads: int. Number of attention heads.
        attention_head_size: int. Size of attention head.
        attention_probs_dropout_prob: float. dropout probability for attention_layer
        intermediate_size: int. Size of intermediate hidden layer.
        intermediate_act_fn: (optional) Activation function for the intermediate
            layer.
        initializer_range: float. Range of the weight initializer.
        hidden_dropout_prob: (optional) float. Dropout probability of the hidden
            layer.
        use_einsum: bool. Whether to use einsum or reshape+matmul for dense layers

    Returns:
        layer output
    """

    with tf.variable_scope("attention_1"):
        with tf.variable_scope("self"):
            attention_output = attention_layer(
                from_tensor=layer_input,
                to_tensor=layer_input,
                attention_mask=attention_mask,
                num_attention_heads=num_attention_heads,
                attention_probs_dropout_prob=attention_probs_dropout_prob,
                initializer_range=initializer_range,
                use_einsum=use_einsum)

        # Run a linear projection of `hidden_size` then add a residual
        # with `layer_input`.
        with tf.variable_scope("output"):
            attention_output = dense_layer_3d_proj(
                attention_output,
                hidden_size,
                attention_head_size,
                create_initializer(initializer_range),
                None,
                use_einsum=use_einsum,
                name="dense")
            attention_output = dropout(attention_output, hidden_dropout_prob)
    attention_output = layer_norm(attention_output + layer_input)
    with tf.variable_scope("ffn_1"):
        with tf.variable_scope("intermediate"):
            intermediate_output = dense_layer_2d(
                attention_output,
                intermediate_size,
                create_initializer(initializer_range),
                intermediate_act_fn,
                use_einsum=use_einsum,
                num_attention_heads=num_attention_heads,
                name="dense")
            with tf.variable_scope("output"):
                ffn_output = dense_layer_2d(
                    intermediate_output,
                    hidden_size,
                    create_initializer(initializer_range),
                    None,
                    use_einsum=use_einsum,
                    num_attention_heads=num_attention_heads,
                    name="dense")
            ffn_output = dropout(ffn_output, hidden_dropout_prob)
    ffn_output = layer_norm(ffn_output + attention_output)
    return ffn_output


def transformer_model(input_tensor,
                      attention_mask=None,
                      hidden_size=768,
                      num_hidden_layers=12,
                      num_hidden_groups=12,
                      num_attention_heads=12,
                      intermediate_size=3072,
                      inner_group_num=1,
                      intermediate_act_fn="gelu",
                      hidden_dropout_prob=0.1,
                      attention_probs_dropout_prob=0.1,
                      initializer_range=0.02,
                      do_return_all_layers=False,
                      use_einsum=True):
    """Multi-headed, multi-layer Transformer from "Attention is All You Need".

    This is almost an exact implementation of the original Transformer encoder.

    See the original paper:
    https://arxiv.org/abs/1706.03762

    Also see:
    https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py

    Args:
        input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
        attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length],
            with 1 for positions that can be attended to and 0 in positions that
            should not be.
        hidden_size: int. Hidden size of the Transformer.
        num_hidden_layers: int. Number of layers (blocks) in the Transformer.
        num_hidden_groups: int. Number of group for the hidden layers, parameters
            in the same group are shared.
        num_attention_heads: int. Number of attention heads in the Transformer.
        intermediate_size: int. The size of the "intermediate" (a.k.a., feed
            forward) layer.
        inner_group_num: int, number of inner repetition of attention and ffn.
        intermediate_act_fn: function. The non-linear activation function to apply
            to the output of the intermediate/feed-forward layer.
        hidden_dropout_prob: float. Dropout probability for the hidden layers.
        attention_probs_dropout_prob: float. Dropout probability of the attention
            probabilities.
        initializer_range: float. Range of the initializer (stddev of truncated
            normal).
        do_return_all_layers: Whether to also return all layers or just the final
            layer.
        use_einsum: bool. Whether to use einsum or reshape+matmul for dense layers

    Returns:
        float Tensor of shape [batch_size, seq_length, hidden_size], the final
        hidden layer of the Transformer.

    Raises:
        ValueError: A Tensor shape or parameter is invalid.
    """
    if hidden_size % num_attention_heads != 0:
        raise ValueError(
            "The hidden size (%d) is not a multiple of the number of attention "
            "heads (%d)" % (hidden_size, num_attention_heads))

    attention_head_size = hidden_size // num_attention_heads
    input_shape = get_shape_list(input_tensor, expected_rank=3)
    input_width = input_shape[2]

    all_layer_outputs = []
    if input_width != hidden_size:
        prev_output = dense_layer_2d(
            input_tensor, hidden_size, create_initializer(initializer_range),
            None, use_einsum=use_einsum, name="embedding_hidden_mapping_in")
    else:
        prev_output = input_tensor
    with tf.variable_scope("transformer", reuse=tf.AUTO_REUSE):
        for layer_idx in range(num_hidden_layers):
            group_idx = int(layer_idx / num_hidden_layers * num_hidden_groups)
            with tf.variable_scope("group_%d" % group_idx):
                with tf.name_scope("layer_%d" % layer_idx):
                    layer_output = prev_output
                    for inner_group_idx in range(inner_group_num):
                        with tf.variable_scope("inner_group_%d" % inner_group_idx):
                            layer_output = attention_ffn_block(
                                layer_input=layer_output,
                                hidden_size=hidden_size,
                                attention_mask=attention_mask,
                                num_attention_heads=num_attention_heads,
                                attention_head_size=attention_head_size,
                                attention_probs_dropout_prob=attention_probs_dropout_prob,
                                intermediate_size=intermediate_size,
                                intermediate_act_fn=intermediate_act_fn,
                                initializer_range=initializer_range,
                                hidden_dropout_prob=hidden_dropout_prob,
                                use_einsum=use_einsum)
                            prev_output = layer_output
                            all_layer_outputs.append(layer_output)
    if do_return_all_layers:
        return all_layer_outputs
    else:
        return all_layer_outputs[-1]


def get_shape_list(tensor, expected_rank=None, name=None):
    """Returns a list of the shape of tensor, preferring static dimensions.

    Args:
        tensor: A tf.Tensor object to find the shape of.
        expected_rank: (optional) int. The expected rank of `tensor`. If this is
            specified and the `tensor` has a different rank, and exception will be
            thrown.
        name: Optional name of the tensor for the error message.

    Returns:
        A list of dimensions of the shape of tensor. All static dimensions will
        be returned as python integers, and dynamic dimensions will be returned
        as tf.Tensor scalars.
    """
    if name is None:
        name = tensor.name

    if expected_rank is not None:
        assert_rank(tensor, expected_rank, name)

    shape = tensor.shape.as_list()

    non_static_indexes = []
    for (index, dim) in enumerate(shape):
        if dim is None:
            non_static_indexes.append(index)

    if not non_static_indexes:
        return shape

    dyn_shape = tf.shape(tensor)
    for index in non_static_indexes:
        shape[index] = dyn_shape[index]
    return shape


def reshape_to_matrix(input_tensor):
    """Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix)."""
    ndims = input_tensor.shape.ndims
    if ndims < 2:
        raise ValueError("Input tensor must have at least rank 2. Shape = %s" % input_tensor.shape)
    if ndims == 2:
        return input_tensor

    width = input_tensor.shape[-1]
    output_tensor = tf.reshape(input_tensor, [-1, width])
    return output_tensor


def reshape_from_matrix(output_tensor, orig_shape_list):
    """Reshapes a rank 2 tensor back to its original rank >= 2 tensor."""
    if len(orig_shape_list) == 2:
        return output_tensor

    output_shape = get_shape_list(output_tensor)

    orig_dims = orig_shape_list[0:-1]
    width = output_shape[-1]

    return tf.reshape(output_tensor, orig_dims + [width])


def assert_rank(tensor, expected_rank, name=None):
    """Raises an exception if the tensor rank is not of the expected rank.

    Args:
        tensor: A tf.Tensor to check the rank of.
        expected_rank: Python integer or list of integers, expected rank.
        name: Optional name of the tensor for the error message.

    Raises:
        ValueError: If the expected shape doesn't match the actual shape.
    """
    if name is None:
        name = tensor.name

    expected_rank_dict = {}
    if isinstance(expected_rank, six.integer_types):
        expected_rank_dict[expected_rank] = True
    else:
        for x in expected_rank:
            expected_rank_dict[x] = True

    actual_rank = tensor.shape.ndims
    if actual_rank not in expected_rank_dict:
        scope_name = tf.get_variable_scope().name
        raise ValueError(
            "For the tensor `%s` in scope `%s`, the actual rank "
            "`%d` (shape = %s) is not equal to the expected rank `%s`" %
            (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))
